
library(tidyverse)
library(nycflights13)


flights %>% head() 
airlines %>% head() # 항공사
airports %>% head() # 공항
planes %>% head() # 여객기
weather %>% head() # 공항의 매 시각 날씨 정보


flights_latlon <- flights %>%
  inner_join(select(airports, origin = faa, origin_lat = lat, origin_lon = lon),
    by = "origin"
  ) %>%
  inner_join(select(airports, dest = faa, dest_lat = lat, dest_lon = lon),
    by = "dest"
  )
flights_latlon %>%
  slice(1:100) %>%
  ggplot(aes(
    x = origin_lon, xend = dest_lon,
    y = origin_lat, yend = dest_lat
  )) +
  borders("state") +
  geom_segment(arrow = arrow(length = unit(0.1, "cm"))) +
  coord_quickmap() +
  labs(y = "Latitude", x = "Longitude")


planes %>% head()


planes %>% count(tailnum) %>% filter(n > 1)


weather %>% count(year, month, day, hour, origin) %>% filter(n > 1)


flights %>% count(year, month, day, flight) %>% filter(n > 1) %>% head()


flights %>% count(year, month, day, tailnum) %>% filter(n > 1) %>% head()


# 10.3.1
# 1 
flights %>%
  arrange(year, month, day, sched_dep_time, carrier, flight) %>%
  mutate(flight_id = row_number()) %>%
  head()


# 2 
library(Lahman)
Batting %>% count(playerID, yearID, stint) %>% filter(n > 1)


library(babynames)
babynames %>% count(year, sex, name) %>% filter(n > 1) %>% nrow()


nasaweather::atmos %>%
  count(lat, long, year, month) %>%
  filter(n > 1) %>% head()


fueleconomy::vehicles %>%
  count(id) %>%
  filter(n > 1) %>% head()


diamonds <- mutate(ggplot2::diamonds, id = row_number())
head(diamonds)


# 3 
head(Master) ; head(Batting) ; head(Salaries)


library(datamodelr)
dm1 <- dm_from_data_frames(list(
  Batting = Lahman::Batting,
  Master = Lahman::Master,
  Salaries = Lahman::Salaries
)) %>%
  dm_set_key("Master", "playerID") %>%
  dm_set_key("Batting", c("playerID", "yearID", "stint")) %>%
  dm_set_key("Salaries", c("yearID", "teamID", "playerID")) %>%
  dm_add_references(
    Batting$playerID == Master$playerID,
    Salaries$playerID == Master$playerID
  )

dm_create_graph(dm1, rankdir = "LR", columnArrows = TRUE) %>%
  dm_render_graph()


head(Master) ; head(Managers) ; head(AwardsManagers)


dm2 <- dm_from_data_frames(list(
  Master = Lahman::Master,
  Managers = Lahman::Managers,
  AwardsManagers = Lahman::AwardsManagers
)) %>%
  dm_set_key("Master", "playerID") %>%
  dm_set_key("Managers", c("yearID", "teamID", "inseason")) %>%
  dm_set_key("AwardsManagers", c("playerID", "awardID", "yearID")) %>%
  dm_add_references(
    Managers$playerID == Master$playerID,
    AwardsManagers$playerID == Master$playerID
  )

dm_create_graph(dm2, rankdir = "LR", columnArrows = TRUE) %>%
  dm_render_graph()


head(Batting) ; head(Fielding) ; head(Pitching)


flights2 <- flights %>% select(year:day, hour, origin, dest, tailnum, carrier)
flights2 %>% head()
head(airlines)


flights2 %>% select(-origin, -dest) %>% left_join(airlines, by = "carrier") %>% head()


x <- tribble(
    ~key, ~val_x,
    1, "x1",
    2, "x2",
    3, "x3"
)
y <- tribble(
    ~key, ~val_y,
    1, "y1",
    2, "y2",
    4, "y3"
)
x ; y


x %>% inner_join(y, by = "key")


x <- tribble(
    ~key, ~val_x,
    1, "x1",
    2, "x2", 
    2, "x3",
    1, "x4"
)
y <- tribble(
    ~key, ~val_y,
    1, "y1", 
    2, "y2"
)

left_join(x, y, by = "key")


x <- tribble(
    ~key, ~val_x,
    1, "x1",
    2, "x2", 
    2, "x3",
    3, "x4"
)
y <- tribble(
    ~key, ~val_y,
    1, "y1", 
    2, "y2",
    2, "y3",
    4, "y4"
)

left_join(x, y, by = "key")


flights2 %>% left_join(weather) %>% head()

Joining, by = c("year", "month", "day", "hour", "origin")


flights2 %>% left_join(planes, by = "tailnum") %>% head()


flights2 %>% left_join(airports, c("dest" = "faa")) %>% head()


flights2 %>% left_join(airports, c("origin" = "faa")) %>% head()


# 10.4.6
# 1
airports %>%
  semi_join(flights, c("faa" = "dest")) %>%
  ggplot(aes(lon, lat)) +
  borders("state") +
  geom_point() +
  coord_quickmap()


avg_dest_delays <- flights %>% group_by(dest) %>% 
    summarise(delay = mean(arr_delay, na.rm = T)) %>% 
    inner_join(airports, by = c("dest" = "faa"))


avg_dest_delays %>% head


avg_dest_delays %>%
  ggplot(aes(lon, lat, colour = delay)) +
  borders("state") +
  geom_point() +
  coord_quickmap()


# 2 
airport_locations <- airports %>%
  select(faa, lat, lon)

flights %>%
  select(year:day, hour, origin, dest) %>%
  left_join(
    airport_locations,
    by = c("origin" = "faa")
  ) %>%
  left_join(
    airport_locations,
    by = c("dest" = "faa")
  ) %>% print

# A tibble: 336,776 x 10
    year month   day  hour origin dest  lat.x lon.x lat.y lon.y
   <int> <int> <int> <dbl> <chr>  <chr> <dbl> <dbl> <dbl> <dbl>
 1  2013     1     1     5 EWR    IAH    40.7 -74.2  30.0 -95.3
 2  2013     1     1     5 LGA    IAH    40.8 -73.9  30.0 -95.3
 3  2013     1     1     5 JFK    MIA    40.6 -73.8  25.8 -80.3
 4  2013     1     1     5 JFK    BQN    40.6 -73.8  NA    NA  
 5  2013     1     1     6 LGA    ATL    40.8 -73.9  33.6 -84.4
 6  2013     1     1     5 EWR    ORD    40.7 -74.2  42.0 -87.9
 7  2013     1     1     6 EWR    FLL    40.7 -74.2  26.1 -80.2
 8  2013     1     1     6 LGA    IAD    40.8 -73.9  38.9 -77.5
 9  2013     1     1     6 JFK    MCO    40.6 -73.8  28.4 -81.3
10  2013     1     1     6 LGA    ORD    40.8 -73.9  42.0 -87.9
# ... with 336,766 more rows


airport_locations <- airports %>%
  select(faa, lat, lon)

flights %>%
  select(year:day, hour, origin, dest) %>%
  left_join(
    airport_locations,
    by = c("origin" = "faa")
  ) %>%
  left_join(
    airport_locations,
    by = c("dest" = "faa"),
    suffix = c("_origin", "_dest")
  ) %>% print

# A tibble: 336,776 x 10
    year month   day  hour origin dest  lat_origin lon_origin lat_dest lon_dest
   <int> <int> <int> <dbl> <chr>  <chr>      <dbl>      <dbl>    <dbl>    <dbl>
 1  2013     1     1     5 EWR    IAH         40.7      -74.2     30.0    -95.3
 2  2013     1     1     5 LGA    IAH         40.8      -73.9     30.0    -95.3
 3  2013     1     1     5 JFK    MIA         40.6      -73.8     25.8    -80.3
 4  2013     1     1     5 JFK    BQN         40.6      -73.8     NA       NA  
 5  2013     1     1     6 LGA    ATL         40.8      -73.9     33.6    -84.4
 6  2013     1     1     5 EWR    ORD         40.7      -74.2     42.0    -87.9
 7  2013     1     1     6 EWR    FLL         40.7      -74.2     26.1    -80.2
 8  2013     1     1     6 LGA    IAD         40.8      -73.9     38.9    -77.5
 9  2013     1     1     6 JFK    MCO         40.6      -73.8     28.4    -81.3
10  2013     1     1     6 LGA    ORD         40.8      -73.9     42.0    -87.9
# ... with 336,766 more rows


# 3
plane_cohorts <- inner_join(flights,
  select(planes, tailnum, plane_year = year),
  by = "tailnum"
) %>%
  mutate(age = year - plane_year) %>%
  filter(!is.na(age)) %>%
  mutate(age = if_else(age > 25, 25L, age)) %>%
  group_by(age) %>%
  summarise(
    dep_delay_mean = mean(dep_delay, na.rm = TRUE),
    dep_delay_sd = sd(dep_delay, na.rm = TRUE),
    arr_delay_mean = mean(arr_delay, na.rm = TRUE),
    arr_delay_sd = sd(arr_delay, na.rm = TRUE),
    n_arr_delay = sum(!is.na(arr_delay)),
    n_dep_delay = sum(!is.na(dep_delay))
  )
plane_cohorts %>% print

# A tibble: 26 x 7
     age dep_delay_mean dep_delay_sd arr_delay_mean arr_delay_sd n_arr_delay
   <int>          <dbl>        <dbl>          <dbl>        <dbl>       <int>
 1     0          10.6          34.4           4.01         38.5        4611
 2     1           9.64         31.9           2.85         37.4        7196
 3     2          11.8          41.8           5.70         46.8        6008
 4     3          12.5          37.5           5.18         41.9        3771
 5     4          11.0          35.5           4.92         39.7        6572
 6     5          13.2          39.6           5.57         43.9       17731
 7     6          13.7          41.4           7.54         45.2       15142
 8     7          14.6          41.3           9.90         45.1       12998
 9     8          14.7          41.5           9.80         45.4       14064
10     9          16.4          44.2          10.2          48.0       15273
# ... with 16 more rows, and 1 more variable: n_dep_delay <int>


ggplot(plane_cohorts, aes(x = age, y = dep_delay_mean)) +
  geom_point() + 
    xlab("Age of Plane (years)") + ylab("Mean Departure Delay (minutes)")


ggplot(plane_cohorts, aes(x = age, y = arr_delay_mean)) +
  geom_point() + 
    xlab("Age of Plane (years)") + ylab("Mean Arrival Delay (minutes)")


# 4
flight_weather <- flights %>% 
    inner_join(weather, by = c("origin" = "origin",
                              "year" = "year",
                              "month" = "month",
                              "day" = "day",
                              "hour" = "hour"))
head(flight_weather)


flight_weather %>%
    group_by(precip) %>% 
    summarise(delay = mean(dep_delay, na.rm = T)) %>%
    ggplot(aes(precip, delay)) + geom_line() + geom_point()


flight_weather %>%
    ungroup() %>%
    mutate(visib_cat = cut_interval(visib, n = 10)) %>%
    group_by(visib_cat) %>%
    summarise(dep_delay = mean(dep_delay, na.rm = T)) %>%
    ggplot(aes(visib_cat, dep_delay)) + geom_point()


# 5
library(viridis)
flights %>%
  filter(year == 2013, month == 6, day == 13) %>%
  group_by(dest) %>%
  summarise(delay = mean(arr_delay, na.rm = TRUE)) %>%
  inner_join(airports, by = c("dest" = "faa")) %>%
  ggplot(aes(y = lat, x = lon, size = delay, colour = delay)) +
  borders("state") +
  geom_point() +
  coord_quickmap() + scale_colour_viridis()

Warning message:
"Removed 3 rows containing missing values (geom_point)."


top_dest <- flights %>%
    count(dest, sort = T) %>% head(10)


flights %>% semi_join(top_dest) %>% print

Joining, by = "dest"

# A tibble: 141,145 x 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      542            540         2      923            850
 2  2013     1     1      554            600        -6      812            837
 3  2013     1     1      554            558        -4      740            728
 4  2013     1     1      555            600        -5      913            854
 5  2013     1     1      557            600        -3      838            846
 6  2013     1     1      558            600        -2      753            745
 7  2013     1     1      558            600        -2      924            917
 8  2013     1     1      558            600        -2      923            937
 9  2013     1     1      559            559         0      702            706
10  2013     1     1      600            600         0      851            858
# ... with 141,135 more rows, and 11 more variables: arr_delay <dbl>,
#   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>


flights %>% anti_join(planes, by = "tailnum") %>% count(tailnum, sort = T) %>% head


# 10.5.1
# 2
planes_gte100 <- flights %>%
  filter(!is.na(tailnum)) %>%
  group_by(tailnum) %>%
  count() %>%
  filter(n >= 100) %>% print()

# A tibble: 1,217 x 2
# Groups:   tailnum [1,217]
   tailnum     n
   <chr>   <int>
 1 N0EGMQ    371
 2 N10156    153
 3 N10575    289
 4 N11106    129
 5 N11107    148
 6 N11109    148
 7 N11113    138
 8 N11119    148
 9 N11121    154
10 N11127    124
# ... with 1,207 more rows


#3
fueleconomy::vehicles %>% print
fueleconomy::common %>% print

# A tibble: 33,442 x 12
      id make  model        year class trans drive   cyl displ fuel    hwy   cty
   <dbl> <chr> <chr>       <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
 1 13309 Acura 2.2CL/3.0CL  1997 Subc~ Auto~ Fron~     4   2.2 Regu~    26    20
 2 13310 Acura 2.2CL/3.0CL  1997 Subc~ Manu~ Fron~     4   2.2 Regu~    28    22
 3 13311 Acura 2.2CL/3.0CL  1997 Subc~ Auto~ Fron~     6   3   Regu~    26    18
 4 14038 Acura 2.3CL/3.0CL  1998 Subc~ Auto~ Fron~     4   2.3 Regu~    27    19
 5 14039 Acura 2.3CL/3.0CL  1998 Subc~ Manu~ Fron~     4   2.3 Regu~    29    21
 6 14040 Acura 2.3CL/3.0CL  1998 Subc~ Auto~ Fron~     6   3   Regu~    26    17
 7 14834 Acura 2.3CL/3.0CL  1999 Subc~ Auto~ Fron~     4   2.3 Regu~    27    20
 8 14835 Acura 2.3CL/3.0CL  1999 Subc~ Manu~ Fron~     4   2.3 Regu~    29    21
 9 14836 Acura 2.3CL/3.0CL  1999 Subc~ Auto~ Fron~     6   3   Regu~    26    17
10 11789 Acura 2.5TL        1995 Comp~ Auto~ Fron~     5   2.5 Prem~    23    18
# ... with 33,432 more rows
# A tibble: 347 x 4
   make  model                n years
   <chr> <chr>            <int> <int>
 1 Acura Integra             42    16
 2 Acura Legend              28    10
 3 Acura MDX 4WD             12    12
 4 Acura NSX                 28    14
 5 Acura TSX                 27    11
 6 Audi  A4                  49    19
 7 Audi  A4 Avant quattro    49    15
 8 Audi  A4 quattro          66    19
 9 Audi  A6                  20    19
10 Audi  A6 Avant quattro    12    12
# ... with 337 more rows


fueleconomy::vehicles %>% 
semi_join(fueleconomy::common, c("make", "model")) %>% 
print

# A tibble: 14,531 x 12
      id make  model    year class     trans drive   cyl displ fuel    hwy   cty
   <dbl> <chr> <chr>   <dbl> <chr>     <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
 1  1833 Acura Integra  1986 Subcompa~ Auto~ Fron~     4   1.6 Regu~    28    22
 2  1834 Acura Integra  1986 Subcompa~ Manu~ Fron~     4   1.6 Regu~    28    23
 3  3037 Acura Integra  1987 Subcompa~ Auto~ Fron~     4   1.6 Regu~    28    22
 4  3038 Acura Integra  1987 Subcompa~ Manu~ Fron~     4   1.6 Regu~    28    23
 5  4183 Acura Integra  1988 Subcompa~ Auto~ Fron~     4   1.6 Regu~    27    22
 6  4184 Acura Integra  1988 Subcompa~ Manu~ Fron~     4   1.6 Regu~    28    23
 7  5303 Acura Integra  1989 Subcompa~ Auto~ Fron~     4   1.6 Regu~    27    22
 8  5304 Acura Integra  1989 Subcompa~ Manu~ Fron~     4   1.6 Regu~    28    23
 9  6442 Acura Integra  1990 Subcompa~ Auto~ Fron~     4   1.8 Regu~    24    20
10  6443 Acura Integra  1990 Subcompa~ Manu~ Fron~     4   1.8 Regu~    26    21
# ... with 14,521 more rows


# 4
worst_hours <- flights %>%
  mutate(hour = sched_dep_time %/% 100) %>%
  group_by(origin, year, month, day, hour) %>%
  summarise(dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
  ungroup() %>%
  arrange(desc(dep_delay)) %>%
  slice(1:48)
worst_hours %>% print

`summarise()` has grouped output by 'origin', 'year', 'month', 'day'. You can override using the `.groups` argument.

# A tibble: 48 x 6
   origin  year month   day  hour dep_delay
   <chr>  <int> <int> <int> <dbl>     <dbl>
 1 LGA     2013     7    28    21      280.
 2 EWR     2013     2     9    10      269 
 3 EWR     2013     2     9     9      266 
 4 LGA     2013     9     2    16      250.
 5 LGA     2013     7    22    18      246.
 6 LGA     2013     7    28    19      240.
 7 JFK     2013     4    10    21      237 
 8 LGA     2013     9    12    20      226.
 9 EWR     2013     3     8    12      225.
10 LGA     2013    12     5    11      221.
# ... with 38 more rows


weather_most_delayed <- semi_join(weather, worst_hours, 
                                  by = c("origin", "year",
                                         "month", "day", "hour")) %>% print

# A tibble: 48 x 15
   origin  year month   day  hour  temp  dewp humid wind_dir wind_speed
   <chr>  <int> <int> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>
 1 EWR     2013     2     9     9  27.0  17.1  65.8      310      13.8 
 2 EWR     2013     2     9    10  28.0  16.0  60.1      310      19.6 
 3 EWR     2013     2     9    11  28.9  16.0  57.9      320      28.8 
 4 EWR     2013     3     8    12  33.8  32    95.8      320       9.21
 5 EWR     2013     3     8    14  34.0  33.1  96.5      320       8.06
 6 EWR     2013     6    30    16  80.1  73.0  79.2      160       8.06
 7 EWR     2013     7    10    19  86    69.1  57.1      250      13.8 
 8 EWR     2013     7    28    19  73.4  71.6  94.1       NA       6.90
 9 EWR     2013     9    12    17  84.0  73.0  69.6      180       5.75
10 EWR     2013     9    12    19  78.8  71.6  93.5      260      18.4 
# ... with 38 more rows, and 5 more variables: wind_gust <dbl>, precip <dbl>,
#   pressure <dbl>, visib <dbl>, time_hour <dttm>


select(weather_most_delayed, temp, wind_speed, precip) %>%
  print

# A tibble: 48 x 3
    temp wind_speed precip
   <dbl>      <dbl>  <dbl>
 1  27.0      13.8    0   
 2  28.0      19.6    0   
 3  28.9      28.8    0   
 4  33.8       9.21   0.06
 5  34.0       8.06   0.05
 6  80.1       8.06   0   
 7  86        13.8    0   
 8  73.4       6.90   0.08
 9  84.0       5.75   0   
10  78.8      18.4    0.23
# ... with 38 more rows


ggplot(weather_most_delayed, aes(x = precip, y = wind_speed, color = temp)) +
  geom_point()


# 5
anti_join(flights, airports, by = c("dest" = "faa")) %>% distinct(dest)


anti_join(airports, flights, by = c("faa" = "dest")) %>% print

# A tibble: 1,357 x 8
   faa   name                             lat    lon   alt    tz dst   tzone    
   <chr> <chr>                          <dbl>  <dbl> <dbl> <dbl> <chr> <chr>    
 1 04G   Lansdowne Airport               41.1  -80.6  1044    -5 A     America/~
 2 06A   Moton Field Municipal Airport   32.5  -85.7   264    -6 A     America/~
 3 06C   Schaumburg Regional             42.0  -88.1   801    -6 A     America/~
 4 06N   Randall Airport                 41.4  -74.4   523    -5 A     America/~
 5 09J   Jekyll Island Airport           31.1  -81.4    11    -5 A     America/~
 6 0A9   Elizabethton Municipal Airport  36.4  -82.2  1593    -5 A     America/~
 7 0G6   Williams County Airport         41.5  -84.5   730    -5 A     America/~
 8 0G7   Finger Lakes Regional Airport   42.9  -76.8   492    -5 A     America/~
 9 0P2   Shoestring Aviation Airfield    39.8  -76.6  1000    -5 U     America/~
10 0S9   Jefferson County Intl           48.1 -123.    108    -8 A     America/~
# ... with 1,347 more rows


# 6
planes_carriers <-
  flights %>%
  filter(!is.na(tailnum)) %>%
  distinct(tailnum, carrier) %>% print

# A tibble: 4,060 x 2
   carrier tailnum
   <chr>   <chr>  
 1 UA      N14228 
 2 UA      N24211 
 3 AA      N619AA 
 4 B6      N804JB 
 5 DL      N668DN 
 6 UA      N39463 
 7 B6      N516JB 
 8 EV      N829AS 
 9 B6      N593JB 
10 AA      N3ALAA 
# ... with 4,050 more rows


planes_carriers %>%
  count(tailnum) %>%
  filter(n > 1) %>% print

# A tibble: 17 x 2
   tailnum     n
   <chr>   <int>
 1 N146PQ      2
 2 N153PQ      2
 3 N176PQ      2
 4 N181PQ      2
 5 N197PQ      2
 6 N200PQ      2
 7 N228PQ      2
 8 N232PQ      2
 9 N933AT      2
10 N935AT      2
11 N977AT      2
12 N978AT      2
13 N979AT      2
14 N981AT      2
15 N989AT      2
16 N990AT      2
17 N994AT      2


carrier_transfer_tbl <- planes_carriers %>%
  group_by(tailnum) %>%
  filter(n() > 1) %>%
  left_join(airlines, by = "carrier") %>%
  arrange(tailnum, carrier)


carrier_transfer_tbl %>% print

# A tibble: 34 x 3
# Groups:   tailnum [17]
   carrier tailnum name                    
   <chr>   <chr>   <chr>                   
 1 9E      N146PQ  Endeavor Air Inc.       
 2 EV      N146PQ  ExpressJet Airlines Inc.
 3 9E      N153PQ  Endeavor Air Inc.       
 4 EV      N153PQ  ExpressJet Airlines Inc.
 5 9E      N176PQ  Endeavor Air Inc.       
 6 EV      N176PQ  ExpressJet Airlines Inc.
 7 9E      N181PQ  Endeavor Air Inc.       
 8 EV      N181PQ  ExpressJet Airlines Inc.
 9 9E      N197PQ  Endeavor Air Inc.       
10 EV      N197PQ  ExpressJet Airlines Inc.
# ... with 24 more rows


df1 <- tribble(
    ~x, ~y,
    1, 1,
    2, 1
)
df2 <- tribble(
    ~x, ~y,
    1, 1,
    1, 2
)


intersect(df1, df2)


union(df1, df2)


setdiff(df1, df2) ; setdiff(df2, df1)

tailnum	year	type	manufacturer	model	engines	seats	speed	engine
<chr>	<int>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<chr>
N10156	2004	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N102UW	1998	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N103US	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N104UW	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N10575	2002	Fixed wing multi engine	EMBRAER	EMB-145LR	2	55	NA	Turbo-fan
N105UW	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan

carat	cut	color	clarity	depth	table	price	x	y	z	id
<dbl>	<ord>	<ord>	<ord>	<dbl>	<dbl>	<int>	<dbl>	<dbl>	<dbl>	<int>
0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43	1
0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31	2
0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31	3
0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63	4
0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75	5
0.24	Very Good	J	VVS2	62.8	57	336	3.94	3.96	2.48	6

	playerID	birthYear	birthMonth	birthDay	birthCountry	birthState	birthCity	deathYear	deathMonth	deathDay	...	weight	height	bats	throws	debut	finalGame	retroID	bbrefID	deathDate	birthDate
	<chr>	<int>	<int>	<int>	<chr>	<chr>	<chr>	<int>	<int>	<int>	...	<int>	<int>	<fct>	<fct>	<chr>	<chr>	<chr>	<chr>	<date>	<date>
1	aardsda01	1981	12	27	USA	CO	Denver	NA	NA	NA	...	215	75	R	R	2004-04-06	2015-08-23	aardd001	aardsda01	NA	1981-12-27
2	aaronha01	1934	2	5	USA	AL	Mobile	2021	1	22	...	180	72	R	R	1954-04-13	1976-10-03	aaroh101	aaronha01	2021-01-22	1934-02-05
3	aaronto01	1939	8	5	USA	AL	Mobile	1984	8	16	...	190	75	R	R	1962-04-10	1971-09-26	aarot101	aaronto01	1984-08-16	1939-08-05
4	aasedo01	1954	9	8	USA	CA	Orange	NA	NA	NA	...	190	75	R	R	1977-07-26	1990-10-03	aased001	aasedo01	NA	1954-09-08
5	abadan01	1972	8	25	USA	FL	Palm Beach	NA	NA	NA	...	184	73	L	L	2001-09-10	2006-04-13	abada001	abadan01	NA	1972-08-25
6	abadfe01	1985	12	17	D.R.	La Romana	La Romana	NA	NA	NA	...	235	74	L	L	2010-07-28	2019-09-28	abadf001	abadfe01	NA	1985-12-17

	yearID	teamID	lgID	playerID	salary
	<int>	<fct>	<fct>	<chr>	<int>
1	1985	ATL	NL	barkele01	870000
2	1985	ATL	NL	bedrost01	550000
3	1985	ATL	NL	benedbr01	545000
4	1985	ATL	NL	campri01	633333
5	1985	ATL	NL	ceronri01	625000
6	1985	ATL	NL	chambch01	800000

	playerID	birthYear	birthMonth	birthDay	birthCountry	birthState	birthCity	deathYear	deathMonth	deathDay	...	weight	height	bats	throws	debut	finalGame	retroID	bbrefID	deathDate	birthDate
	<chr>	<int>	<int>	<int>	<chr>	<chr>	<chr>	<int>	<int>	<int>	...	<int>	<int>	<fct>	<fct>	<chr>	<chr>	<chr>	<chr>	<date>	<date>
1	aardsda01	1981	12	27	USA	CO	Denver	NA	NA	NA	...	215	75	R	R	2004-04-06	2015-08-23	aardd001	aardsda01	NA	1981-12-27
2	aaronha01	1934	2	5	USA	AL	Mobile	2021	1	22	...	180	72	R	R	1954-04-13	1976-10-03	aaroh101	aaronha01	2021-01-22	1934-02-05
3	aaronto01	1939	8	5	USA	AL	Mobile	1984	8	16	...	190	75	R	R	1962-04-10	1971-09-26	aarot101	aaronto01	1984-08-16	1939-08-05
4	aasedo01	1954	9	8	USA	CA	Orange	NA	NA	NA	...	190	75	R	R	1977-07-26	1990-10-03	aased001	aasedo01	NA	1954-09-08
5	abadan01	1972	8	25	USA	FL	Palm Beach	NA	NA	NA	...	184	73	L	L	2001-09-10	2006-04-13	abada001	abadan01	NA	1972-08-25
6	abadfe01	1985	12	17	D.R.	La Romana	La Romana	NA	NA	NA	...	235	74	L	L	2010-07-28	2019-09-28	abadf001	abadfe01	NA	1985-12-17

key¶

Master¶

Batting¶

Salaries¶

Master¶

Managers¶

AwardsManagers:¶

Mutating joins¶

내부 조인 (inner join)¶

외부 조인 (outer join)¶

중복키¶

경우 1¶

경우 2¶

자연 조인 (natural join)¶

여객기의 나이와 지연 시간 사이의 관계¶

merge() & SQL¶

필터링 조인¶

조인 문제¶

집합 연산¶

carrier	name
<chr>	<chr>
9E	Endeavor Air Inc.
AA	American Airlines Inc.
AS	Alaska Airlines Inc.
B6	JetBlue Airways
DL	Delta Air Lines Inc.
EV	ExpressJet Airlines Inc.

tailnum	n
<chr>	<int>
NA	2512
N725MQ	575
N722MQ	513
N723MQ	507
N713MQ	483
N735MQ	396

A data.frame: 6 × 10
	playerID	yearID	teamID	lgID	inseason	G	W	L	rank	plyrMgr
	<chr>	<int>	<fct>	<fct>	<int>	<int>	<int>	<int>	<int>	<fct>
1	wrighha01	1871	BS1	NA	1	31	20	10	3	Y
2	woodji01	1871	CH1	NA	1	28	19	9	2	Y
3	paborch01	1871	CL1	NA	1	29	10	19	8	Y
4	lennobi01	1871	FW1	NA	1	14	5	9	8	Y
5	deaneha01	1871	FW1	NA	2	5	2	3	8	Y
6	fergubo01	1871	NY2	NA	1	33	16	17	5	Y

A data.frame: 6 × 6
	playerID	awardID	yearID	lgID	tie	notes
	<chr>	<chr>	<int>	<fct>	<chr>	<lgl>
1	larusto01	BBWAA Manager of the Year	1983	AL	NA	NA
2	lasorto01	BBWAA Manager of the Year	1983	NL	NA	NA
3	andersp01	BBWAA Manager of the Year	1984	AL	NA	NA
4	freyji99	BBWAA Manager of the Year	1984	NL	NA	NA
5	coxbo01	BBWAA Manager of the Year	1985	AL	NA	NA
6	herzowh01	BBWAA Manager of the Year	1985	NL	NA	NA

A data.frame: 6 × 18
	playerID	yearID	stint	teamID	lgID	POS	G	GS	InnOuts	PO	A	E	DP	PB	WP	SB	CS	ZR
	<chr>	<int>	<int>	<fct>	<fct>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	abercda01	1871	1	TRO	NA	SS	1	1	24	1	3	2	0	NA	NA	NA	NA	NA
2	addybo01	1871	1	RC1	NA	2B	22	22	606	67	72	42	5	NA	NA	NA	NA	NA
3	addybo01	1871	1	RC1	NA	SS	3	3	96	8	14	7	0	NA	NA	NA	NA	NA
4	allisar01	1871	1	CL1	NA	2B	2	0	18	1	4	0	0	NA	NA	NA	NA	NA
5	allisar01	1871	1	CL1	NA	OF	29	29	729	51	3	7	1	NA	NA	NA	NA	NA
6	allisdo01	1871	1	WS3	NA	C	27	27	681	68	15	20	4	18	NA	0	0	NA

A tibble: 3 × 2
key	val_x
<dbl>	<chr>
1	x1
2	x2
3	x3